The main contribution is to utilize attention to derive adaptive adjacency matrices which are then utilized in RNNs to capture spatio-temporal correlations among time series to enable accurate forecasting for correlated time series.
The proposed graph attention is a generic approach which can be applied to any graph-based convolution operations that utilizes adjacency matrices such as graph convolution and diffusion convolution [1]}.
Experiments on a large real-word traffic time series offer evidence that the proposed method is accurate and robust.
